Title: Road sign detection using edited shuffled frogs leaping algorithm

Authors: Ameur Zaibi; Anis Ladgham; Anis Sakly

Addresses: Laboratory of Automation, Electrical Systems and Environment (LAESE), Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia; National Engineering School of Monastir (LAESE), University of Monastir, Monastir, Tunisia ' Laboratory of Automation, Electrical Systems and Environment (LAESE), Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia; Laboratory Electronic and Microelectronic (EμE), Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia ' Laboratory of Automation, Electrical Systems and Environment (LAESE), Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia; National Engineering School of Monastir LAESE, University of Monastir, Monastir, Tunisia

Abstract: This paper suggests a new system for the automated detection of road signs. This driver assistance system detects traffic signs that have a red border with different shapes (circular, triangular, hexagonal). So, our approach relies on Support Vector Machines (SVM) implementation for road signs detection supported by feature extraction technique supported employment of a range of filters from Gabor that simplifies the recognition of interest's points in our database. On the other hand, our approach has been improved on the Edited Shuffled Frogs Leaping Algorithm (ESFLA) optimisation technique that helps in road signs detection and this technique is termed Gabor-ESFLA-SVM. This strategy ensures an intelligent recognition system. The obtained results show that this optimised classification provides higher results compared to the previous dual classification Gabor-SVM and other research works published in a few articles.

Keywords: road signs with red border detection; ESFLA optimisation; optimal solution; Gabor wavelets; features extraction; Gabor representation; SVM classification; fitness function.

DOI: 10.1504/IJVS.2021.115888

International Journal of Vehicle Safety, 2021 Vol.12 No.1, pp.1 - 14

Received: 14 Jan 2020
Accepted: 07 Sep 2020
Published online: 05 Jul 2021 *

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